Empirical drag closure for polydisperse gas–liquid systems in bubbly flow regime: Bubble swarm and micro-scale turbulence

Buffo, Antonio; Vanni, Marco; Renze, Peter; Marchisio, Daniele

doi:10.1016/j.cherd.2016.08.004

Cited by 29 publications

(12 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A similar result is in line with our previous validation work[25], where different pieces of gas-liquid equipment operating in the bubbly flow regime as in this case were studied. Moreover, it is also important to remark the fact that in a CFD-PBM the bubble size is not a parameter of the simulation, but it is calculated through the PBM: although for this case there are no measurements of bubble size, the good agreement obtained for the fluid dynamic quantities is an indirect confirmation of the model capability of capturing the physical reality.4.2.…”

supporting

confidence: 89%

“…In Fig. 7 [70], as we consider this as our "standard" settings since it was proven to give good agreement with the experiments on many other test cases [25]. Figure 10 it is possible to notice that the two curves are overlapped in the first 60 s, as also the comparison between simulation and experiment for the OH − concentration reported in Fig.…”

Section: Experiments Without Chemical Reactionsmentioning

confidence: 71%

“…The second correction is instead related to the so-called micro-scale turbulence, namely the turbulence on a length-scale smaller than the bubble size (that is therefore not resolved by the standard κ − model) as discussed in the literature [45], resulting again in an increase of the drag force coefficient [46]. Extensive comparison with experiments on different test cases working in the bubbly flow regime showed that a general good agreement can be obtained by using C A = −1.3 and C B = 0.002 [25].…”

Section: Governing Equationsmentioning

confidence: 80%

“…The drag model considered in this work is based on the following expression for the drag force coefficient [25]:…”

Section: Governing Equationsmentioning

confidence: 99%

“…Different methods can be divided into two large families: sectionalbased [11,12,13,14,15,16,17,18], in which the bubble size distribution is divided into bins and the evolution of the number of the bubbles per each bin is acconted for, and moment-based [19,20,21,22,23,24,25], in which transport equations for some integral properties of the population of bubbles (i.e., moments of the bubble size distribution) are tracked. For an overview of different methods, readers may refer to specialized literature [1,26,27] Moment-based methods have the advantage of making use of a less number of addtional equations being suitable for the solution of large scale problems [28,29].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Simulation of a reacting gas–liquid bubbly flow with CFD and PBM: Validation with experiments

Buffo

Vanni

Marchisio

2017

Applied Mathematical Modelling

Self Cite

View full text Add to dashboard Cite

Highlights • QMOM is implemented in the OpenFOAM v. 2.2.x solver compressibleTwoPhaseEulerFoam • The set-up is used to simulate a gas-liquid bubble column involving a fast chemical reaction • The operator splitting procedure is used to deal with the fast chemical reactions • Simulation predictions are predicted with experiments resulting in good agreement 1 ACCEPTED MANUSCRIPT AbstractIn this work we use computational fluid dynamics (CFD) to simulate a reactive gas-liquid bubbly system in a rectangular bubble column, operating at low superficial velocities (i.e. homogeneous regime). The gas bubbles, injected in the column through a sparger, contain one of the reactants, namely CO 2 , that via mass transfer moves to the continuous liquid phase, where it reacts with NaOH. A key role is played by the bubble size distribution (BSD) and the specific surface area that define the overall mass transfer rate in the CFD model. In order to correctly predict the BSD and the polydispersity of the bubbly system the population balance equation is solved by the quadrature method of moments (QMOM), within the OpenFOAM (v. 2.2.x) two-fluid solver compressibleTwoPhaseEulerFoam. To reduce the computational time and increase stability, a second-order operator-splitting technique for the solution of the chemically reactive species is also implemented, allowing to solve the different processes involved with their own time-scale. To

show abstract

supporting

confidence: 89%

Section: Experiments Without Chemical Reactionsmentioning

confidence: 71%

Section: Governing Equationsmentioning

confidence: 80%

“…The drag model considered in this work is based on the following expression for the drag force coefficient [25]:…”

Section: Governing Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Simulation of a reacting gas–liquid bubbly flow with CFD and PBM: Validation with experiments

Buffo

Vanni

Marchisio

2017

Applied Mathematical Modelling

Self Cite

View full text Add to dashboard Cite

show abstract

Simulated oxygen and glucose gradients as a prerequisite for predicting industrial scale performance a priori

Kuschel

2020

Biotech & Bioengineering

View full text Add to dashboard Cite

Transferring bioprocesses from lab to industrial scale without loss of performance is key for the successful implementation of novel production approaches. Because mixing and mass transfer is usually hampered in large scale, cells experience heterogeneities eventually causing deteriorated yields, that is, reduced titers, productivities, and sugar‐to‐product conversions. Accordingly, reliable and easy‐to‐implement tools for a priori prediction of large‐scale performance based on dry and wet‐lab tests are heavily needed. This study makes use of computational fluid dynamic simulations of a multiphase multi‐impeller stirred tank in pilot scale. So‐called lifelines, records of 120,000 Corynebacterium glutamicum cells experiencing fluctuating environmental conditions, were identified and used to properly design wet‐lab scale‐down (SD) devices. Physical parameters such as power input, gas hold up, , and mixing time showed good agreement with experimental measurements. Analyzing the late fed‐batch cultivation revealed that the complex double gradient of glucose and oxygen can be translated into a wet‐lab SD setup with only few compartments. Most remarkably, the comparison of different mesh sizes outlined that even the coarsest approach with a mesh density of was sufficient to properly predict physical and biological readouts. Accordingly, the approach offers the potential for the thorough analysis of realistic industrial case scenarios.

show abstract

A Review of Drag Coefficient Models in Gas‐Liquid Two‐Phase Flow

et al. 2023

View full text Add to dashboard Cite

Although great efforts have been made in modeling the drag force in gas‐liquid two‐phase flow, the research about that in both theory and method is at the stage of development due to its diversity and complexity, and previous summary work about drag is usually based on a certain aspect. In this review, a unified framework that guides the drag coefficient model in gas‐liquid two‐phase flow is provided. From simple to complex, this paper introduces the drag coefficient of solid particles, single bubbles, bubble swarms, and bubbles in turbulence moving in liquid in turn. The variables affecting the drag coefficient in each of the above situations and the relationship between the drag coefficient and these variables are mentioned.

show abstract

Empirical drag closure for polydisperse gas–liquid systems in bubbly flow regime: Bubble swarm and micro-scale turbulence

Cited by 29 publications

References 70 publications

Simulation of a reacting gas–liquid bubbly flow with CFD and PBM: Validation with experiments

Simulation of a reacting gas–liquid bubbly flow with CFD and PBM: Validation with experiments

Simulated oxygen and glucose gradients as a prerequisite for predicting industrial scale performance a priori

A Review of Drag Coefficient Models in Gas‐Liquid Two‐Phase Flow

Contact Info

Product

Resources

About